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Gesture Recognition / Sign Language

Gesture Recognition / Sign Language. Lukas Bloder Johannes Bannhofer SE09 MUS2 SS10. Overview. Hardware Sign Language Live Demo System Architecture System Tools Technologies Problems Fazit. Hardware. P5 Glove API: http://www.robotgroup.org/index.cgi/P5Glove. CyberGlove :

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Gesture Recognition / Sign Language

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  1. Gesture Recognition / Sign Language Lukas Bloder Johannes Bannhofer SE09 MUS2 SS10

  2. Overview • Hardware • SignLanguage • Live Demo • System Architecture • System Tools • Technologies • Problems • Fazit

  3. Hardware P5 Glove API: http://www.robotgroup.org/index.cgi/P5Glove CyberGlove: http://www.immersion.com http://www.golem.de/0512/42086.html MIT Color Glove Handtracking http://people.csail.mit.edu/rywang/handtracking/

  4. Hardware CyberGlove :

  5. Hardware • Number of sensors: 18 or 22 • Sensor Resolution: 0.5 degrees (typical) • Sensor Data Rate: 90 records/sec minimum (100 records/sec typical). • Operating system andhosts: Windows 2000 and XP • Operating Range: 30 ft radius from USB port • Interface: USB port for the wireless receiver CyberGlove II:

  6. Sign Language • American manual alphabet

  7. Sign Language • Substitution signs • Dynamic signs: J, Z • Additional Signs • Space, enter, delete, variouscommands

  8. Demo Time!

  9. System • C++ API (Partiallyfrom original sourceof 1998) • JNI Bridge • Application: • Exchangeable Processing (Matlab, weka) • Rules (substitutionsigns, comamnds) • Clients (Commandline, TTS, Graphical)

  10. System Architecture

  11. Classification using ANN • Matlabnntool

  12. Classification using ANN • Matlab – Erros recognizing letters

  13. Processing Rules • Rules to process more complex signs • Recognition splitted to Wrist/Fingers • Evaluation with rules

  14. System Tools • Data Collector • Data Aggregator

  15. Technologies Used • C++ / Java • Matlab • MaryTTS

  16. Problems • Old API • Matlab /generating JAR Files • API license problems • Training data • Inconsistent sensor data

  17. Fazit • Old Hardware still does the job • Don’t touch machine generated code • Generating good training data -> hard work

  18. Thanks for your attention! Lukas Bloder Johannes Bannhofer SE09 PEG SS10

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